Automatic High-Level Test Case Generation using Large Language Models
This program is tentative and subject to change.
We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case → (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software’s functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4, Gemini, LLaMA 3.1, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development.
This program is tentative and subject to change.
Tue 29 AprDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | |||
14:00 10mTalk | Automatic High-Level Test Case Generation using Large Language Models Technical Papers Navid Bin Hasan Bangladesh University of Engineering and Technology, Md. Ashraful Islam Bangladesh University of Engineering and Technology, Junaed Younus Khan Bangladesh University of Engineering and Technology, Sanjida Senjik Bangladesh University of Engineering and Technology, Anindya Iqbal Bangladesh University of Engineering and Technology Dhaka, Bangladesh | ||
14:10 10mTalk | Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories Technical Papers Mahan Tafreshipour University of California at Irvine, Aaron Imani University of California, Irvine, Eric Huang University of California, Irvine, Eduardo Santana de Almeida Federal University of Bahia, Thomas Zimmermann University of California, Irvine, Iftekhar Ahmed University of California at Irvine Pre-print | ||
14:20 10mTalk | Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution Technical Papers Ramtin Ehsani Drexel University, Sakshi Pathak Drexel University, Preetha Chatterjee Drexel University, USA Pre-print | ||
14:30 10mTalk | Intelligent Semantic Matching (ISM) for Video Tutorial Search using Transformer Models Technical Papers | ||
14:40 10mTalk | Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Technical Papers Negar Alizadeh Universiteit Utrecht, Boris Belchev University of Twente, Nishant Saurabh Utrecht University, Patricia Kelbert Fraunhofer IESE, Fernando Castor University of Twente | ||
14:50 10mTalk | TriGraph: A Probabilistic Subgraph-Based Model for Visual Code Completion in Pure Data Technical Papers Anisha Islam Department of Computing Science, University of Alberta, Abram Hindle University of Alberta | ||
15:00 5mTalk | Inferring Questions from Programming Screenshots Technical Papers Faiz Ahmed York University, Xuchen Tan York University, Folajinmi Adewole York University, Suprakash Datta York University, Maleknaz Nayebi York University | ||
15:05 5mTalk | Human-In-The-Loop Software Development Agents: Challenges and Future Directions Industry Track Jirat Pasuksmit Atlassian, Wannita Takerngsaksiri Monash University, Patanamon Thongtanunam University of Melbourne, Kla Tantithamthavorn Monash University, Ruixiong Zhang Atlassian, Shiyan Wang Atlassian, Fan Jiang Atlassian, Jing Li Atlassian, Evan Cook Atlassian, Kun Chen Atlassian, Ming Wu Atlassian | ||
15:10 5mTalk | FormalSpecCpp: A Dataset of C++ Formal Specifications Created Using LLMs Data and Tool Showcase Track Madhurima Chakraborty University of California, Riverside, Peter Pirkelbauer Lawrence Livermore National Laboratory, Qing Yi Lawrence Livermore National Laboratory | ||